Study the Impact of Global Change on Air Quality using the Global-through-Urban Weather Research and Forecast with ChemistryPrakash Karamchandani ENVIRON, Inc. (formerly at AER, Inc.)
David G. Streets Argonne National Laboratory
Recent Progress in Developing A Global-Through- Urban Weather Research and Forecast Model with
Chemistry (GU-WRF/Chem)
• Model Development Highlights • Current-Year Simulations
• Model Application and Evaluation • Chemistry-Aerosol-Cloud-Radiation Feedbacks – Direct Effects on Shortwave Radiation and NO2 Photolysis – Semi-Direct Effects on Planetary Boundary Layer (PBL) Meteorology – Indirect Effects on Cloud Condensation Nuclei (CCN), Cloud Droplet Number
Concentration (CDNC) & Precipitation • Future-Year Simulations
• Impact of Projected Emissions and Climate Change on Air Quality • GU-WRF/Chem vs. Community Climate System Model (CCSM)
• Major Findings and Future Work
Development and Application of Global-through-Urban Weather Research and Forecasting Model with Chemistry (GU-WRF/Chem):
Hypotheses, Objectives, and Scientific Questions
• Hypothesis – Climate change (CC) - air quality (AQ) feedbacks are important
• Objectives and Tasks – Develop a unified online-coupled model for integrated CC-AQ modeling – Conduct global-through-urban simulations for current/future scenarios – Replicate/quantify CC-AQ feedbacks and examine model uncertainties – Guide the win-win strategy for integrated CC mitigation and AQ control
• Scientific Questions – What are the important feedbacks of urban/regional air pollutants to CC? – How can CC and emission control affect urban/regional AQ? – What are key uncertainties associated with predicted effects/feedbacks?
Development and Application of Global-through-Urban Weather Research and Forecasting Model with Chemistry (GU-WRF/Chem)
Model Development and Application Activities • Key Model Development
– Globalize WRF/Chem – Compile global emissions (MOZART4, RETRO, IPCC, AeroCom); project
future-year emissions based on IPCC A1B – Develop/improve model treatments for global-through-urban applications
– Incorporate SAPRC99/CB05/CB05_GE, MADRID, FN05, & nucleation – Couple gaseous mechanisms with default and new aerosol/cloud modules – Add/improve other treatments (e.g., FTUV, dust, SOA, plume-in-grid)
• Model Evaluation of Current-Year (2001) Simulations – Met: T, QV, Precip, Radiation from NCEP/NCAR, NCDC, CMAP, TRMM, BSRN – Chem: O3 and PM2.5 from CASTNET, STN, IMPROVE, AIRS-AQS, SEARCH
column CO, NO2, and TOR from MOPITT, GOME, OMI, TOMS/SBUV – Other: AOD, CCN, CDNC, Cloud Fraction, COT, CER from MODIS
• Model Intercomparison and Trend Analysis of Future-Year Simulations – Intercomparison: 2050 GU-WRF/Chem vs. 2046-2055 10-yr average CCSM – Trend Analysis: 2010, 2020, 2030, 2040, and 2050 vs. 2001
Development and Incorporation of CB05 for Global Extension (CB05_GE) into GU-WRF/Chem
• Box Model Test – Four conditions: urban, upper troposphere, lower stratosphere, and Arctic – Several scenarios: NoClBr – no halogen chemistry (blue), ClBr – with full
halogen chemistry (red), NoBr – with chlorine chemistry (green)
• A Total of 120 New Reactions in CB05_GE – 5 stratospheric reactions (O2, N2O, O1D) – 78 reactions for 25 halogen species (48 for 14 Cl and 30 for 11 Br species) – 4 mercury reactions (Hg(0) and Hg(II)) – 13 heterogeneous reactions on aerosol/cloud and 20 reactions on PSCs – H2O, CH4, CO2, O2 and H2 are treated as chemically-reactive species
Arctic (March)
O3 Hg(II)Hg(0)
Simulated Aerosol Activation Fractions as a Function of Parcel Temperature and Updraft Velocity:
Uncertainty in Aerosol Activation Parameterizations • Two Activation Parameterizations
Abdul Razzak-Ghan 2000 (AR-G00) (Default in WRF/Chem) Fountoukis-Nenes 2005 (F-N05)
• Box Model Test Single aerosol type (sulfate), with a modal representation with 3 modes Identical CCN spectrum in AR-G00 and F-N05 3 conditions: Marine (Type 1), Continental (Type 2), Remote Marine (Type 3)
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300 295 290 285 280 275 270 265 260 255 250
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Temperature (K)
Activation Fraction as a Function of Parcel Temperature AR-G Type 1 F-N Type 1 AR-G Type 2 F-N Type 2 AR-G Type 3 F-N Type 3
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0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
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Updraft Velocity (m/s)
Activation Fraction as a Function of Parcel Updraft Velocity AR-G Type 1 F-N Type 1 AR-G Type 2 F-N Type 2 AR-G Type 3 F-N Type 3
Aerosol activation fractions differ by up to a factor of 4
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0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
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Accomendation Coefficient
Activation Fraction as a Function of Accomendation Coefficient AG Type 1 NF Type 1 AG Type 2 NF Type 2 AG Type 3 NF Type 3
Parcel Temperature Updraft Velocity Accommodation Coefficient
Simulated Nucleation Rates as a Function of NH2SO4 Uncertainty in Nucleation Parameterizations
Nucleation rates differ by > 16 orders of magnitude
1.E-08
1.E-07
1.E-06
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
1.E+01
1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E+07
1.E+08
1.E+09
1.E+10
1.E+11
1.E+12
1.E+04 1.E+05 1.E+06 1.E+07 1.E+08 1.E+09 1.E+10 1.E+11 1.E+12
Nu cle
at io
NH2SO4, molecules cm-3
Nucleation Rate as a Function of NH2SO4, T = 298.15 K, RH=80% Pandis et al., 1994
Fitzgerald et al., 1998
Harrington and Kreidenweis, 1998
Kulmala et al., 1998
Vehkamaki et al., 2002
Yu, 2006, NH3=0.1-100 ppt
Sihto et al., 2006
Kuang et al., 2008
1000-50 mb)
• Domain: D01: 4 × 5, 45 (lat.) × 72 (long.) (Global) D02: 1.0 × 1.25, 44 × 192 (Trans-Pacific) D03-CONUS: 0.33 × 0.42, 84× 168 (CONUS) D03-China: 99× 177 (China) D04: 0.08 × 0.10, 136× 144 (E. US)
D01: Global D02: Trans-Pacific D03: CONUS and China D04: E. US
D02
D01
D04 - EUS
• Period: Met only: 2001/2050, at 4 × 5 & 1 × 1, w different physics options
Gas and PM: 1. 2001 Jan/Jul over D01-D04, w and w/o PM 2. 2001, 2010, 2020, 2030, 2040, 2050 over D01
2001 Monthly Mean Daily Precipitation (mm/day)
Jan.
Direct Effects of PM2.5 on Shortwave Radiation
PM2.5 decreases shortwave radiation domainwide by up to -45% (global mean: -10%)
Absolute DifferenceJan. Jul.
Semi-Direct Effects of PM2.5 on Temperature at 2-m
PM2.5 decreases T2 over most areas up to -546% (global mean: -1.6%)
Absolute DifferenceJan. Jul.
Indirect Effects of PM2.5 on Precipitation
PM2.5 decrease precipitation over polluted regions by up to -82% (global mean: -5%)
Jan. Jul.
Absolute Difference
Indirect Effects of PM2.5 on Column CCN (S=1%) Jan. Jul.
Jan.
Jul.
Absolute Difference
PM2.5 enhances CCN domainwide by up to 3340% (global mean: 478%)
Major Findings and Future Work • GU-WRF/Chem demonstrates promising skills in reproducing observations • Aerosol feedbacks to radiation, meteorology, and cloud microphysics
– Aerosols decrease shortwave radiation by up to -45% (global mean: -10%) – Aerosols decrease NO2 photolysis rate by up to -52% (global mean: -11%) – Aerosols decrease near-surface temperature by up to -546% (global mean: -1.6%) – Aerosols decrease PBL height by up to -39% (global mean: -1.7%) – Aerosols increase to CCN by up to 3340% (global mean: 478%) – Aerosols increase to CDNC by up to 5751% (global mean: 318%) – Aerosols decrease precipitation by up to -82% (global mean: -5%)
• Simulated aerosol, radiation, and cloud properties exhibit small-to-high sensitivity to nucleation and aerosol activation parameterizations
– Higher sensitivity to nucleation parameterizations: PM mass and number, CCN, Precip – Higher sensitivity to activation parameterizations: AOD, COT, CDNC, LWP, Reff – Small sensitivity: OLR, GLW, GSW, SWDOWN, RSWTOA, CF
• Observations are needed to verify feedbacks, improve models, and reduce the uncertainties in simulated aerosol direct and indirect effects
• Use feedbacks to guide win-win emission control strategies for CC/AQ – Isolate and quantify complex speciated feedbacks: GHGs, cooling and warming PM – Assess the effectiveness of O3 and PM attainment plans under different future emission
scenarios and a changing climate
Acknowledgments
• Project sponsor: EPA STAR #R83337601 • Mark Richardson, Caltech, William C. Skamarock, NCAR, for sharing global
WRF, and Louisa Emmons & Francis Vitt, NCAR, for CAM4 emissions • Georg Grell, Steve Peckham, and Stuart McKeen, NOAA/ESRL, for public
release of WRF/Chem • Jerome Fast, Steve Ghan, Richard Easter, and Rahul Zaveri, PNNL, for public
release of PNNL’s version of WRF/Chem • Ken Schere, Golam Sarwar, and Shawn Roselle, U.S. EPA, for providing CB05
and CB05Cltx, and Shaocai Yu, U.S. NOAA/EPA, for providing Fortran code for statistical calculation
• Athanasios Nenes, Georgia Tech, for providing aerosol activation code • Fangqun Yu, SUNKat Albany, for proving nucleation lookup tables • Jack Fishman and John K. Creilson, NASA LRC, for providing TOR data • Ralf Bennartz, University of Wisconsin – Madison, for providing CDNC • Peter McMurry, University of Minnesota, for providing PM nucleation data
Slide Number 1
Development and Incorporation of CB05 for Global Extension (CB05_GE) into GU-WRF/Chem
Simulated Aerosol Activation Fractions as a Function of Parcel Temperature and Updraft Velocity: Uncertainty in Aerosol Activation Parameterizations
Simulated Nucleation Rates as a Function of NH2SO4 Uncertainty in Nucleation Parameterizations
Nested GU-WRF/Chem Simulations(Base Configurations: FTUV/CB05GE/MADRID/CMU/AR-G, 27 layers from 1000-50 mb)
Slide Number 9
Semi-Direct Effects of PM2.5 on Temperature at 2-m
Indirect Effects of PM2.5 on Precipitation
Indirect Effects of PM2.5 on Column CCN (S=1%)
Major Findings and Future Work
Acknowledgments